认知MIMO网络中增强型干扰对齐算法

针对认知多输入多输出（MIMO）网络中传统基于最大信干噪比的干扰对齐算法，在发送多数据流时随着信噪比的增加不易收敛以及数据流之间的干扰突出的问题，提出一种充分考虑数据流间干扰并进行迭代限制的干扰对齐算法。首先，次用户通过编码设计消除主次间的干扰；然后，在消除主用户之间和次用户之间干扰时，根据信道互易性，运用广义瑞利熵计算基于最大信干噪比算法的预编码与干扰抑制矩阵，并在迭代过程中，每次迭代始终使预编码与干扰抑制矩阵先满足干扰功率在期望信号空间最小；最后，结合次用户间MIMO干扰信道、主次用户间构成的MIMO干扰信道以及次用户网络干扰对齐的必要性，推导出次用户可达自由度上限。实验结果表明，相比传统最大信干噪比算法，所提算法在信噪比较低时次用户总容量无明显提高，但随着信干噪比的增加其优势越来越明显；当达到收敛时，所提算法迭代次数比传统最大信干噪比算法约减少40%。因此，所提算法能够提高系统容量且加快收敛。

Enhanced interference alignment algorithm in cognitive MIMO network

Aiming at the problems that traditional interference alignment algorithm based on the maximum Signal to Interference and Noise Ratio (SINR) in Multiple-Input Multiple-Output (MIMO) cognitive network is hard to converge when sending multiple data streams and the interference between them is prominent, an interference alignment algorithm that considers data stream interference and iterative limit was proposed. Firstly, the secondary users eliminated interference between primary users and secondary users through coding design. Then, when eliminating the interference between the primary users and the secondary users, the Generalized Rayleigh Entropy (GRE) was used to calculate the precoding and interference suppression matrix based on the maximum SINR algorithm according to channel reciprocity, and in the iterative process, each iteration always made precoding and interference suppression matrix firstly satisfy that the interference power in the expected signal space was minimal. Finally, combined with the MIMO interference channel between the secondary users, the interference channel between primary and secondary users and the necessity of interference alignment of secondary usernetwork, the secondary users' reachable upper bound of degree of freedom was deduced. The experimental results show that compared with the traditional maximum SINR algorithm, the proposed algorithm has no significant improvement in the total capacity of the secondary users when the signal to noise ratio is low, but with the increase of signal to noise ratio, the advantages of the proposed algorithm are more and more obvious. When convergence is reached, the iterative times of the proposed algorithm are reduced by 40% compared with the conventional maximum SINR algorithm. Therefore, the proposed algorithm can improve system capacity and accelerate convergence.